Where is your POSEIDON now?
Ernesto Carrella
July 26, 2017
A minute of POSEIDON
- POSEIDON is an agent-based model
- POSEIDON has a few use cases:
- Scenario evalutation
- Optimization
- Reiforcement Learning
Work in progress
- Calibrating and validating West-Coast DTS model
- Develop the infrastructure for Indonesia
- Data degradation for West-Coast fixed gear model
Calibration
- We have two target data-sets:
- Landings/quota attainments
- Logbook data
- Calibrate in two steps
- Fix agents statistically
- Find catchabilities that generate correct quotas
- Fix catchabilities but let agents act adaptively
- Find behavioural parameters that replicate logbook
- How realistic can explore-exploit-imitate agents possibly be?
Calibration - Results

Calibration - Results 2
- Looks realistic with simple adaptive agents
- Adaptive agents are very risk-averse:
- Calibrated exploration rate is 3.5%
Calibration - 15% exploration rate

Calibration - 3
- It’s trivial to find more performing agents:
- Are we picking up an approximation error from the way we distribute fish compared to the real world?
- Are fishers just not that profit maximizing?
Fixed Gear Allocation
